Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools

Mark Cieliebak, Oliver Dürr, Fatih Uzdilli


Abstract
In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.
Anthology ID:
L14-1634
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3100–3104
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/820_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Mark Cieliebak, Oliver Dürr, and Fatih Uzdilli. 2014. Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3100–3104, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools (Cieliebak et al., LREC 2014)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/820_Paper.pdf